Motion state based mobile device positioning

ABSTRACT

Various techniques are provided which may be implemented as methods, apparatuses and articles of manufacture for use by a mobile device or one or more computing devices to provide for or otherwise support motion state based mobile device positioning. In an example, a method may be implemented at a mobile device to identify two or more subsets of grid points corresponding to an electronic map representing a particular environment, select one of the two or more subsets of grid points for use in position estimation based, at least in part, on a motion state of the mobile device, and determine an estimated position of the mobile device based, at least in part, on the selected subset of grid points.

BACKGROUND

1. Field

The subject matter disclosed herein relates to electronic devices, andmore particularly to methods, apparatuses and articles of manufacturefor use by and/or in a mobile device and/or one or more computingdevices to provide for and/or otherwise support motion state basedmobile device positioning.

2. Information

As its name implies, a mobile device may be moved about, e.g. typicallybeing carried by a user and/or possibly a machine. By way of somenon-limiting examples, a mobile device may take the form of a cellulartelephone, a smart phone, a tablet computer, a laptop computer, awearable computer, a navigation and/or tracking device, etc.

A position and/or movements of a mobile device may be determined, atleast in part, by a positioning and/or navigation capability (hereinafter simply referred to as a positioning capability) that may beimplemented on board the mobile device, in one or more other electronicdevices, and/or some combination thereof. Certain positioningcapabilities may be based on one or more wireless signals transmitted byone or more transmitting devices and acquired by mobile device. By wayof example, certain wireless signal-based positioning capabilities makeuse of wireless signals acquired from a satellite positioning system(SPS), such as, e.g., the global positioning system (GPS), etc.

In another example, certain wireless signal-based positioningcapabilities make use of wireless signals acquired fromterrestrial-based wireless transmitting devices, such as, e.g., adedicated positioning Beacon transmitting device, an access point (AP)device which may be part of a wireless local area network, a basetransceiver station which may be part of the cellular telephone system,and/or the like or some combination thereof. In certain implementations,a positioning capability may make use of one or more electronic files,such as, e.g., an electronic map, a routability graph, a radio heatmap,and/or the like or some combination thereof, to determine a positionand/or other movements of the mobile device within a particularenvironment.

Since mobile devices tend to operate on battery power, there's often adesire to serve battery power while providing a reasonable userexperience. Accordingly, there is an ongoing desire to providehigh-quality yet efficient positioning capabilities/processes within amobile device.

SUMMARY

In accordance with an aspect, a method may be implemented at a mobiledevice, which comprises: identifying two or more subsets of grid pointscorresponding to an electronic map representing a particularenvironment; selecting one of the two or more subsets of grid points foruse in position estimation based, at least in part, on a motion state ofthe mobile device; and determining an estimated position of the mobiledevice within the particular environment based, at least in part, on theselected subset of grid points.

In accordance with an aspect, a mobile device may be provided, whichcomprises: memory; and a processing unit to: identify two or moresubsets of grid points stored in the memory as corresponding to anelectronic map representing a particular environment; select one of thetwo or more subsets of grid points for use in position estimation based,at least in part, on a motion state of the mobile device; and determinean estimated position of the mobile device within the particularenvironment based, at least in part, on the selected subset of gridpoints.

In accordance with an aspect, a method may be implemented at a computingdevice, which comprises: obtaining a set of grid points corresponding toan electronic map representing a particular environment; subdividing theset of grid points to identify two or more subsets of grid points foruse in position estimation by a mobile device based, at least in part,on two or more motion states corresponding to the mobile device; andtransmitting at least one of the two or more subsets of grid points tothe mobile device.

In accordance with an aspect a computing device may be provided whichcomprises: a communication interface; and a processing unit to: obtain aset of grid points corresponding to an electronic map representing aparticular environment; subdivide the set of grid points to identify twoor more subsets of grid points for use in position estimation by amobile device based, at least in part, on two or more motion statescorresponding to the mobile device; and initiate transmission of atleast one of the two or more subsets of grid points to the mobile devicevia the communication interface.

BRIEF DESCRIPTION OF DRAWINGS

Non-limiting and non-exhaustive aspects are described with reference tothe following figures, wherein like reference numerals refer to likeparts throughout the various figures unless otherwise specified.

FIG. 1 is a schematic block diagram illustrating an example arrangementof representative electronic devices including an example mobile deviceand an example computing device one both of which may be configured toprovide for and/or otherwise support motion state based mobile devicepositioning, in accordance with certain example implementations.

FIG. 2A and FIG. 2B are flow diagrams illustrating some exampleprocesses that may be implemented in a mobile device, e.g., as in FIG.1, to provide for and/or otherwise support motion state based mobiledevice positioning, in accordance with certain example implementations.

FIG. 3A and FIG. 3B are flow diagrams illustrating some exampleprocesses that may be implemented in a computing device, e.g., as inFIG. 1, to provide for and/or otherwise support motion state basedmobile device positioning, in accordance with certain exampleimplementations.

FIG. 4 is a schematic diagram illustrating certain features of anexample computing platform that may be provisioned within a mobiledevice, e.g., as in FIG. 1, in accordance with certain exampleimplementations.

FIG. 5 is a schematic diagram illustrating certain features of anexample computing platform that may be provisioned within a computingdevice, e.g., as in FIG. 1, in accordance with certain exampleimplementations.

DETAILED DESCRIPTION

Mobile devices may be configured, for example, to obtain all or part ofa position fix and/or the like by measuring ranges to terrestrialtransmitting devices (e.g., wireless access points) which may bepositioned at known and/or otherwise determinable locations. Such rangesmay be measured, for example, by obtaining a MAC ID address from signalsreceived from such access points and measuring one or morecharacteristics of signals received from such access points such as, forexample, signal strength, round trip delay, just to name a few examples.Such measurements may be viewed as “direct measurements” in that theygive information regarding a current position (such as a range to atransmitter fixed at a known position) when obtained.

Typically, measurements of ranges to three transmitters may besufficient for obtaining a sufficiently accurate estimate of a positionof the mobile device. In a particular example where measurements ofranges to more than three transmitters may be available, the accuracy ofsuch an estimate may vary considerably based upon which particularmeasurements to which particular transmitting devices are selected forobtaining such an estimate of a position of a mobile device. Forexample, an inaccuracy in a range measurement to at least onetransmitting device may in certain instances significantly degrade theaccuracy of an estimate of a position of a mobile device computed basedon the range measurement.

In addition to the use of direct measurements, certain mobile devicesmay incorporate “indirect measurements”, which may be indicative ofrelative motion, to assist in obtaining an estimate of a currentposition estimate. Such indirect measurements may include, for example,measurements obtained from signals generated by one or more sensors suchas, for example, accelerometers, pedometers, compasses, gyroscopes,and/or the like or some combination thereof. Also, in certainenvironments and applications, movement of a mobile device may beconstrained by physical obstructions to predetermined areas or paths. Inan indoor environment, for example, movement of a mobile device may beconstrained to predetermined paths or routes defined according to walls,doorways, entrances, stairways, etc. As such, a current location of amobile device may be presumed to be constrained by such predeterminedareas or paths.

A motion model may process direct or indirect measurements to propagatean estimated state of a mobile device (e.g., position, velocity,trajectory, etc.). Such a motion model may comprise, for example, afiltering model such as a Kalman filter or particle filter to propagateestimated positions of a mobile device along one or more trajectories.In a particular implementation, a mobile station may employ aparticle-filter over a constrained routing graph and/or the like toincorporate direct and indirect measurements subject to routeconstraints. For example, in certain instances, route constraints mayindicate that particles may be propagated along a routing graphaccording to the particle state, and certain indirect information aboutrelative position movement, each particle may be assigned a probabilityaccording to direct measurements, and/or particles may be resampledand/or otherwise process according to a probability distribution.

In certain instances, probabilities assigned to particular particlesrepresenting particular locations of an indoor area may comprise valuesindicative of a likelihood that a particular mobile device may belocated at particular positions. For example, for a mobile deviceco-located with a user (e.g., a person, an animal, a machine) havingjust entered a particular doorway of a building and/or other likestructure, a particle filter model and/or the like may assign higherprobabilities to particles representing physicians nearer to the doorwayand relatively lower probabilities representing locations farther awayfrom the doorway, e.g., perhaps on the opposite side of the buildingfrom the doorway. Some particle filter models may, for example, assignprobabilities to particular particles based on an estimated position andvelocity of the mobile device. Unfortunately, an estimated velocity of amobile device may be based on a series of measurements in the past. Witha possibility for quick starts and stops, a velocity estimate computedbased on a series of past measurements may, at times, prove stale orobsolete by the time the velocity estimate is computed.

In certain example implementations as provided herein, a mobile devicemay have one or more sensors such as inertial sensors (e.g., one or moreaccelerometers, pedometers or gyroscopes) and/or other types of sensors(e.g., light sensors, sound transducers, etc.) that may generate signalsindicative of (and/or of particular use in determining) a motion stateof the mobile device. For example, in certain instances, a motion stateof a mobile device may be indicative of a non-transitional (e.g.,substantially static) state or a transitional (e.g., a moving) state,and/or possibly different identifiable types of non-transitional and/ortransitional states (e.g., walking versus running, sitting still at adesk versus sitting still on a moving mechanism/machine. Such anindication of a various types of movement or a lack thereof may, forexample, represent a more reliable and immediate indication of a motionstate of the mobile device than, for example, a velocity estimatedcomputed based on a series of past measurements. According to aparticular implementation, a mobile device may re-weight probabilitiesassigned to particular particles based, at least in part, on motionstate of the mobile device detected in response to signals from one ormore sensors.

In certain example implementations, certain positions in a particularenvironment represented by particles may be classified as either“non-transitional grid points” or “transitional grid points” based, atleast in part, on a likelihood that a user would be moving (e.g.,walking or running) or being relatively static (e.g., sitting, standingor lying down) at such positions within the particular environment. Forexample, corridors or hallways may be associated with transitional gridpoints while offices or rooms may be associated with non-transitionalgrid points. As described in greater detail herein, in certain exampleimplementations, one or more motion state weighting parameters may beassociated with one or more grid points, e.g., for use in identifyingsubsets of grid points which may be of particular use by a mobile devicebased on its motion state. It may be observed that if a user co-locatedwith a mobile device is in dynamic state (e.g., walking or running asindicated by a signal from a sensor), the user may be more likely to belocated at or nearby a transitional grid point rather than anon-transitional grid point. Likewise, it may be observed that if a userco-located with a mobile device is in more of a static state, the usermay be more likely to be located at or nearby a non-transitional gridpoint.

In certain example implementations, values indicative of likelihoodsthat said mobile device is located in particular portions of an area(e.g., probabilities assigned to particular particles in a particlefiltering scheme), may be altered and/or otherwise affected based, atleast in part, on whether one or more signals from a sensor indicatethat the mobile device is in a particular transitional state or aparticular non-transitional state. As pointed out above, signals from anaccelerometer or pedometer may, for example, indicate that a userco-located with the mobile device may be walking or running, orotherwise moving in a particular identifiable and/or classifiablemanner. This may infer that the mobile device has an increasedlikelihood of being located at a transitional grid point. Likewise, ifone or more signals from a sensor indicate that a user co-located withthe mobile device is in a static state, this may infer that the mobiledevice has an increased likelihood of being located at anon-transitional grid point. In certain instances, likelihoods orprobabilities assigned to particles representing transit points may beincreased in response to sensor signals indicating that the mobiledevice is in a secular transitional state (while likelihoods orprobabilities assigned to particles representing non-transitional gridpoints may be decreased). On the other hand, likelihoods orprobabilities assigned to particles representing non-transitional gridpoints may be increased in response to certain sensor signals, e.g.,indicating that the mobile device is in a non-transitional state (whilelikelihoods or probabilities assigned to particles representingtransitional grid points may be decreased).

With the preceding examples in mind, several techniques are describedherein by way of example which may be implemented as various methods,apparatuses and articles of manufacture for use by and/or in a mobiledevice and/or one or more computing devices to provide for and/orotherwise support motion state based mobile device positioning.

By way of an example, in certain implementations, a mobile device (e.g.,a mobile telephone, smart phone, a tablet computer, a wearable computer,a tracking device, etc.) may be configured identify two or more subsetsof grid points corresponding to an electronic map representing aparticular environment. As described in greater detail, in certaininstances, a mobile device may receive one or more subsets of gridpoints from one or more other devices which may have already beenidentified. For example, a mobile device may obtain one or more datafiles from one or more servers over network, which comprise and/or mayotherwise be indicative of a plurality of subsets of grid pointscorresponding to an electronic map of a particular environment (e.g.,all or part of one or more buildings or other structures, etc.).

In certain example implementations, a mobile device may be configured toreceive a set of grid points corresponding to the electronic map from atleast one other device (e.g., a computing device such as a server,etc.), and subdivide the set of grid points in some manner to identifyone or more subsets of grid points.

As described in greater detail herein, in certain instances a mobiledevice may subdivide (e.g., physically and/or logically) a set of gridpoints based, at least in part, on at least one motion state weightingparameter (e.g., representing a likelihood for more or less motion,representing a likelihood for particular type of motion, etc.) for atleast one grid point within the set of grid points. In certain exampleimplementations, a mobile device may subdivide a set of grid pointsbased, at least in part, on a motion state historical record (e.g.,based on reported (e.g., estimated, expected, observed, etc.) motionstate(s) from one or more mobile devices.

Those skilled in the art should recognize that an initial set of gridpoints may be provided in at least one data file and that certainexample subdividing techniques may “logically” alter the data file(s) insome manner to indicate all or part of one or more identified subsets ofgrid points. In certain instances, one or more additional data files maybe “physically” generated which may comprise and/or otherwise beindicative in some manner of all or part of one or more identifiedsubsets of grid points. Of course, these are just a few examples andclaimed subject matter is not intended to be so limited.

As described in greater detail herein, in certain exampleimplementations, a mobile device may be configured to identify one ormore subsets of grid points based, at least in part, on a comparison ofat least one motion state weighting parameter with at least one motionstate threshold value.

By way of example, in certain implementations, a first subset of gridpoints may be identified for use in position estimation by a mobiledevice while in a transitional state, and a second subset of grid pointsmay be identified for use in position estimation by the mobile devicewhile in different transitional state or possibly a non-transitionalstate. Here, for example, a first subset of grid points may be ofparticular use to a mobile device for position estimation while themobile device may be actively transported, e.g., the mobile device mayhave a motion state indicative of some transition within a particularenvironment, such as, while being carried by a person running, walking,climbing/descending stairs, and/or being conveyed by some machine insome active manner, etc. As may be appreciated, such a first subset ofgrid points may correspond to certain regions within a structure whereina mobile device may be more likely to be transitioning from one positionto another position, such as, e.g., a hallway, a walkway, an assemblyline, etc. A second subset of grid points may, however, be of particularuse to a mobile device for position estimation while the mobile devicemay be, less actively transported, e.g., the mobile device may have amotion state indicative of some substantially static/stationary state.May be appreciated, such a second subset of grid points may correspondto certain regions within a structure wherein a mobile device may bemore likely to be placed, set down, stored, and/or aperson/animal/machine may reside otherwise remain completely or somewhat stationary/static or some period of time. For example, anon-transitional region may comprise an office, a break room, waitingroom, a loading zone, a kiosk device, a restroom, a desk, a seat, atreadmill, a book or storage shelf, etc.

With two or more subsets of grid points having been identified, a mobiledevice may then select at least one of the subsets of grid points foruse in position estimation, e.g., based, at least in part, on a motionstate of the mobile device. A mobile device may, for example, determinea motion state based, at least in part, on one or more signals generatedby and/or otherwise obtained from one or more sensors (e.g., anaccelerometer, a gyroscope, a barometer, a light sensor, a soundtransducer, etc.), just to name a few examples.

Having selected at least one of the subsets of grid points for use inposition estimation, a mobile device may then determine some aspect ofits estimated position within the particular environment, e.g., based,at least in part, on a selected subset of grid points.

As described in greater detail herein, in certain exampleimplementations, a mobile device may transmit one or more messages oneor more other devices, which may be indicative of a motion state of themobile device, a motion state weighting parameter (e.g., for at leastone grid point within at least one of the two or more subsets of gridpoints), at least one motion state threshold value, and/or the like orsome combination thereof, again just to name a few examples.

In certain instances, two or more subsets of grid points may be mutuallyexclusive, while in other instances two or more subsets of grid pointsmay comprise one or more common grid points. In certain instances, twoor more subsets of grid points may correspond to different grid patterns(e.g., having different offsets/spacing, having differentgranularity/resolution, etc.). In certain example implementations, allor part of one or more subsets of grid points may be identified in arouting graph and/or the like corresponding to at least a portion of anelectronic map and which may be of use for positioning, navigation,location based services, etc.

In certain example implementations, similar and/or other correspondingtechniques may be implemented at one or more computing devices. By wayof example, in certain implementations a computing device (e.g., aserver, etc.) may obtain a set of grid points corresponding to anelectronic map representing a particular environment, and subdivide(e.g., physically and/or logically) the set of grid points to identifytwo or more subsets of grid points for use in position estimation by oneor more mobile devices based, at least in part, on two or more motionstates corresponding to one or more mobile devices. The computing devicemay, for example, transmit (e.g., identify, provide, etc.) one or moreof subsets of grid points to one or more other devices and/or one ormore mobile devices.

Attention is drawn next to FIG. 1, which is a schematic block diagramillustrating an example arrangement 100 of representative electronicdevices including an example mobile device 102 comprising an exampleapparatus 104 and an example computing device 112 comprising an exampleapparatus 114, one or more of which may be configured to provide forand/or otherwise support motion state based mobile device positioning,in accordance with certain example implementations.

Mobile device 102 may, for example, comprise a portable computingdevice, portable communication device, a portable tracking/locationdevice, and/or the like or some combination thereof. Hence, in certaininstances, mobile device 102 may comprise a cellular telephone, a smartphone, a laptop computer, a tablet computer, a navigation device, awearable computer, a tracking mechanism, just to name a few examples.

As illustrated mobile device 102 may receive wireless signals over acommunication link 111 from one or more networks 110, which may befurther coupled to one or more other devices 116 via communication link117. In certain implementations, network(s) 110 may be representative ofone or more wireless communication systems, one or more cellularcommunication systems, one or more wired communication systems, one ormore computer networks, all or part of the Internet, an intranet, alocal area network, and/or various other computing and/or communicationresources/devices/services.

Mobile device 102 may receive wireless signals over communication link107 from one or more transmitting devices 106, one or more of which maybe further coupled together and/or to network(s) 110 (connection notshown). Transmitting device(s) 106 may be representative of variety ofdifferent transmitting devices and/or transmitting/receiving devicesthat may transmit and/or receive wireless signals. In certainimplementations, transmitting device(s) 106 may comprise one or moretransmitting devices that may be part of or otherwise support network(s)110 or some portion thereof. Hence, for example, transmitting device(s)106 may represent a cellular base station, a femtocell device, a picocell device, a WLAN access point device, a location Beacon device,and/or the like or some combination thereof, just to name a fewexamples. Indeed, in certain instances, transmitting device(s) 106 mayrepresent one or more other mobile devices. In accordance with certainaspects, transmitting device(s) 106 may represent any electronic devicethat may transmit and/or receive wireless signals in support of variouscomputing, communication, location, and/or other likeservices/capabilities provided or otherwise supported by mobile device102. As illustrated, one or more transmitting devices 106 may be locatedwithin a particular environment 101, and/or otherwise operativelyarranged to serve all or part of particular environment 101.

In certain implementations, as shown in FIG. 1, a mobile device 102 mayreceive or acquire SPS signals 134 from one or more space vehicles (SVs)132, which may be part of one or more SPS 130. In some embodiments, SPS130 may be from one global navigation satellite system (GNSS), such asthe GPS or Galileo satellite systems. In other embodiments, the SVs 132may be from multiple GNSS such as, but not limited to, GPS, Galileo,Glonass, or Beidou (Compass) satellite systems. In other embodiments,the SVs 132 may be from any one several regional navigation satellitesystems (RNSS′) such as, for example, Wide Area Augmentation System(WAAS), European Geostationary Navigation Overlay Service (EGNOS),Quasi-Zenith Satellite System (QZSS), just to name a few examples.

In particular implementations, and as discussed below, mobile device 102may have circuitry and processing resources capable of computing aposition fix or estimated position (e.g., a location) of mobile device102. For example, mobile device 102 may compute a position fix based, atleast in part, on pseudorange measurements to one or more or more theSVs 132. Here, mobile device 102 may compute such pseudorangemeasurements based, at least in part, on pseudonoise code phasedetections in SPS signals 134 acquired from four or more SVs 132. Inparticular implementations, mobile device 102 may receive frompositioning assistance data and/or the like from a server (e.g.,represented by other device(s) 116) which may be used to aid in theacquisition of SPS signals 134 including, for example, almanac,ephemeris data, Doppler search windows, just to name a few examples. Incertain implementations, similar other types of positioning assistancedata may be obtained by mobile device 102 from one or more other deviceswith regard to one or more transmitting device(s) 106.

In certain example implementations, mobile device 102 may obtain aposition fix by processing signals received from terrestrialtransmitting device(s) 106 (one or more of which may have fixed and/orotherwise determinable locations) using any one of several techniquessuch as, for example, advanced forward trilateration (AFLT) and/orOTDOA. In these particular example techniques, a range from mobiledevice 102 may be measured to one or more or more of such terrestrialtransmitters fixed at known locations based, at least in part, on pilotsignals transmitted by the transmitting device(s) 106 from fixedotherwise determinable locations and received at mobile device 102. Incertain example stations, as mentioned, one or more other device(s) 116may be capable of providing certain types of positioning assistance datato mobile device 102. By way of example, certain types of positioningassistance data may be indicative of locations and identities ofterrestrial transmitting devices, which may facilitate positioningtechniques such as AFLT and OTDOA. For example, a server represented byother device(s) 116 may provide all or part of a base station almanac(BSA) and/or the like, which may be indicative of locations andidentities of cellular base stations, etc., in a particular region orregions.

In particular environments such as indoor environments or urban canyons,mobile device 102 may not be capable of adequately acquiring SPS signals134 from a sufficient number of SVs 132 and/or two perform AFLT or OTDOAto compute a position fix from acquisition of signals from applicableoutdoor terrestrial transmitting devices. Hence, in certain instances,mobile device 102 may be capable of computing a position fix based, atleast in part, on wireless signals acquired from other transmittingdevices, e.g., local/indoor transmitting devices (e.g., WLAN accesspoints, femto cell transceivers, Bluetooth devices, etc., which may bepositioned at known or otherwise determinable locations). Accordingly,in certain implementations, mobile device 102 may obtain all or part ofa position fix by measuring ranges to one or more indoor terrestrialwireless access point devices and/like. Such ranges may be measured, forexample, by obtaining a MAC ID address from wireless signals receivedfrom such a transmitting device and obtaining range measurements to thetransmitting device (e.g., at least in part, by measuring one or morecharacteristics of the received signals). By way of example, in certainimplementations a received signal strength (RSSI), a round trip time(RTT), an angle of arrival (AOA), and/or the like or some combinationthereof may be determined/considered. In certain implementations, mobiledevice 102 may obtain an indoor position fix by applying characteristicsof acquired signals to a radio heatmap indicating expected RSSI and/orRTT signatures at particular locations in an indoor area. In particularimplementations, a radio heatmap may associate identities of localtransmitters (e.g., a MAC address and/or some other distinctly uniqueidentifier which may be discernible from a signal acquired from a localtransmitter), expected RSSI from signals transmitted by the identifiedlocal transmitting devices, an expected RTT from the identifiedtransmitting devices, and possibly standard deviations from theseexpected RSSI or RTT. It should be understood, however, that these aremerely examples of values that may be stored in, modeled, and/orotherwise functionally/mathematically represented by a radio heatmapand/or the like, and that claimed subject matter is not limited in thisrespect.

In addition to measurements obtained from the acquisition of wirelesssignals from local transmitting devices, according to a particularembodiment, mobile device 102 may further apply a motion model tomeasurements or inferences obtained from inertial sensors (e.g.,accelerometers, gyroscopes, magnetometers, etc.) and/or environmentsensors (e.g., temperature sensors, microphones, barometric pressuresensors, ambient light sensors, camera imager, etc.) in estimating allor part of a position and/or a motion state of mobile device 102.

Arrangement 100 further includes example computing device 112 comprisingapparatus 114, which may communicate with mobile device 102, e.g., viacommunication link 115, network(s) 110, and communication link 111, orpossibly in a more direct manner as represented by indication link 113.As previously mentioned, in certain example implementations, mobiledevice 102 may select a particular subset of grid points for use inposition estimation based, at least in part, on its motion state. Incertain instances, one or more subsets of grid points may be identifiedand provided by computing device 112 and/or apparatus 114 to mobiledevice 102 and/or apparatus 104. In some instances, mobile device 102and/or apparatus 104 may identify one or more subsets of grid points,e.g., based on a larger set of grid points which may be obtained fromcomputing device 112 and/or one or more other devices 116. Whileillustrated as being outside of particular environment 101, it should beunderstood that all or part of computing device 112, other devices 116,and/or network(s) 110 may be provisioned within a particular environment101.

As shown in FIG. 1, certain instances a particular environment 101 maycomprise all or part of one or more structures having one or moreregions therein through which and/or within which a mobile device may bemoved/positioned. By way of example, a floor plan 150 is illustrated fora portion of a building having various regions, with various obstaclesand/or other aspects which may affect movement and/or positioning of amobile device in some manner. Additionally, floor plan 150 isillustrated as having an example overlay of grid points 154, one or moreof which may correspond to particular types of transitional grid pointsand/or non-transitional grid points. By way of example, in certaininstances some grid points within region 156 (e.g., which comprises a“lobby” and adjoining hallways separating office suites A and B) and/orregion 160 (e.g., which comprises an elevator mechanism serving multiplefloors within a structure) may be identified as representing certaintypes of transitional grid points. Conversely, by way of example, incertain instances some grid points within region 158 (e.g., whichcomprises an office or room) may be identified as representing certaintypes of non-transitional grid points. While in this example all of thegrid points 154 are fairly uniform in their arrangement, it should bekept in mind that in certain other implementations grid points may bearranged in a variety of different ways, some of which may be more orless uniform than others, and which may vary in different configurationsdepending upon the region within the structure.

Attention is drawn next to FIG. 2A, which is a flow diagram illustratingan example process 200 that may be implemented in mobile device 102and/or apparatus 104, e.g., to provide for and/or otherwise supportmotion state based mobile device positioning. At example block 202, twoor more subsets of grid points corresponding to an electronic maprepresenting a particular environment may be identified. At exampleblock 204, one of the two or more subsets of grid points may be selectedfor use in position estimation based, at least in part, on a motionstate of the mobile device. At example block 206, an estimated positionof the mobile device within the particular environment may be determinedbased, at least in part, on the selected subset of grid points.

Attention is drawn next to FIG. 2B, which is a flow diagram illustratingan example process 200′ that may be implemented in mobile device 102and/or apparatus 104, e.g., to provide for and/or otherwise supportmotion state based mobile device positioning. In this example, someoptional/alternative techniques are illustrated using dashed-lineblocks.

At example block 202, two or more subsets of grid points correspondingto an electronic map representing a particular environment may beidentified. In certain instances, at example block 208, at least onepreviously identified subset of grid points may be received from atleast one other device, e.g., computing device 112, other device(s) 116,network(s) 110, transmitting device(s) 106, and/or the like himcombination thereof. In certain instances, at block 210 a set of gridpoints corresponding to the electronic map may be received from at leastone other device, and subdivided (e.g., physically and/or logically) toidentify at least one subset of grid points. In certain instances, atblock 212, a set of grid points may be subdivided based, at least inpart, on at least one motion state weighting parameter, and/or at leastone motion state historical record. In certain instances, at block 214,at least one motion state weighting parameter may be compared with atleast one motion state threshold value.

At example block 204, one of the subsets of grid points may be selectedfor use in position estimation based, at least in part, on a motionstate of the mobile device. In certain instances, at block 216, a motionstate may be determined based, at least in part, on at least one signalobtained from at least one sensor of the mobile device. In certaininstances, at block 218, a first subset of grid points may be selectedbased, at least in part, on the motion state indicating that the mobiledevice is in a transitional state. In certain instances, at block 220, asecond subset of grid points may be selected based, at least in part, onthe motion state indicating that the mobile device is in anon-transitional state.

At example block 206, an estimated position of the mobile device withinthe particular environment may be determined based, at least in part, onthe selected subset of grid points.

As part of and/or in addition to example blocks 202, 204, and/or 206, atleast one motion state weighting parameter for at least one grid pointwithin at least one of the subsets of grid points may be affected (e.g.,changed, established, deleted, etc.) based, at least in part, on themotion state of the mobile device, e.g., as illustrated at example block222. For example, a motion state weighting parameter may be affected insome manner to better indicate and/or otherwise adjust/update certainmovements or lack thereof by mobile device while located at or nearby aparticular grid point.

As part of and/or in addition to example blocks 202, 204, and/or 206, asillustrated at example block 224, at least one message may betransmitted to another device to indicate a motion state of the mobiledevice, a motion state weighting parameter for at least one grid pointwithin at least one of the subsets of grid points, and/or at least onemotion state threshold value. In this manner, for example, mobile device102 may provide feedback to another device, e.g., computing device 112,which may support techniques provided herein for identifying varioussubsets of grid points, and/or which may support other applicabletechniques that apply feedback, crowd sourcing, other types of datagathering.

Attention is drawn next to FIG. 3A, which is a flow diagram illustratingan example process 300 that may be implemented in a computing device 112and/or apparatus 114 (FIG. 1), e.g., to provide for and/or otherwisesupport motion state based mobile device positioning, in accordance withcertain example implementations implementation. At example block 302, aset of grid points corresponding to an electronic map representing aparticular environment may be obtained. For example, in certaininstances computing device 112 may obtain all or part of a set of gridpoints from other devices 116. At example block 304, the set of gridpoints may be subdivided (e.g., physically and/or logically) to identifytwo or more subsets of grid points for use in position estimation by amobile device based, at least in part, on two or more motion statescorresponding to the mobile device. At example block 306, at least oneof the two or more subsets of grid points may be transmitted ((e.g.,directly or indirectly) to one or more mobile devices.

Attention is drawn next to FIG. 3B, which is a flow diagram illustratingan example process 300′ that may be implemented in computing device 112and/or apparatus 114, e.g., to provide for and/or otherwise supportmotion state based mobile device positioning. In this example, someoptional/alternative techniques are illustrated using dashed-lineblocks.

At example block 302, a set of grid points corresponding to anelectronic map representing a particular environment may be obtained.For example, in certain instances computing device 112 may obtain all orpart of a set of grid points from other devices 116.

At example block 304, the set of grid points may be subdivided (e.g.,physically and/or logically) to identify two or more subsets of gridpoints for use in position estimation by a mobile device based, at leastin part, on two or more motion states corresponding to the mobiledevice. In certain instances, at example block 308, the set of gridpoints may be subdivided based, at least in part, on at least one motionstate weighting parameter. In certain instances, at example block 310,at least one motion state weighting parameter may be compared with atleast one motion state threshold value. In certain instances, at exampleblock 312, a first subset of grid points may be identified for use inposition estimation by the mobile device while in a transitional state,and a second subset of grid points may be identified for use in positionestimation by the said mobile device while in a non-transitional state.In certain instances, at example block 314, a first subset of gridpoints may be identified for use in position estimation by the mobiledevice while in a first transitional state, and a second subset of gridpoints may be identified for use in position estimation by the saidmobile device while in a second transitional state.

At example block 306, at least one of the two or more subsets of gridpoints may be transmitted ((e.g., directly or indirectly) to one or moremobile devices.

As part of and/or in addition to example blocks 302, 304, and/or 306, asillustrated at example block 316, at least one message may be receivedfrom at least one mobile device, which indicates at least one of: amotion state of the mobile device, a motion state weighting parameterfor at least one grid point within at least one of the two or moresubsets of grid points, and/or at least one motion state thresholdvalue.

FIG. 4 is a schematic diagram illustrating certain features of anexample special purpose computing platform 400 that may be providedwithin computing device 112 and/or apparatus 114 (FIG. 1) according tothe various techniques provided herein.

As illustrated special purpose computing platform 400 may comprise oneor more processing units 402 (e.g., to perform data processing inaccordance with certain techniques provided herein) coupled to memory404 via one or more connections 406 (e.g., one or more electricalconductors, one or more electrically conductive paths, one or morebuses, one or more fiber-optic paths, one or more circuits, one or morebuffers, one or more transmitters, one or more receivers, etc.).Processing unit(s) 402 may, for example, be implemented in hardware or acombination of hardware and software. Processing unit(s) 402 may berepresentative of one or more circuits configurable to perform at leasta portion of a data computing procedure or process. By way of examplebut not limitation, a processing unit may include one or moreprocessors, controllers, microprocessors, microcontrollers, applicationspecific integrated circuits, digital signal processors, programmablelogic devices, field programmable gate arrays, or the like, or anycombination thereof.

Memory 404 may be representative of any data storage mechanism. Memory404 may include, for example, a primary memory 404-1 and/or a secondarymemory 404-2. Primary memory 404-1 may comprise, for example, a randomaccess memory, read only memory, etc. While illustrated in this exampleas being separate from the processing units, it should be understoodthat all or part of a primary memory may be provided within or otherwiseco-located and coupled with processing unit 402 or other like circuitrywithin computing device 112. Secondary memory 404-2 may comprise, forexample, the same or similar type of memory as primary memory and/or oneor more data storage devices or systems, such as, for example, a diskdrive, an optical disc drive, a tape drive, a solid motion state memorydrive, etc.

In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, a non-transitorycomputer readable medium 420. Memory 404 and/or non-transitory computerreadable medium 420 may comprise instructions 422 for use in performingdata processing, e.g., in accordance with the applicable techniques asprovided herein.

Special purpose computing platform 400 may, for example, furthercomprise a communication interface 408. Communication interface 408 may,for example, comprise one or more wired and/or wireless networkinterface units, radios, modems, etc., represented here by one or morereceivers 410 and one or more transmitters 412. It should be understoodthat in certain implementations, communication interface 408 maycomprise one or more transceivers, and/or the like. Further, it shouldbe understood that although not shown, communication interface 408 maycomprise one or more antennas and/or other circuitry as may beapplicable given the communication interface capability.

In accordance with certain example implementations, communicationinterface 408 may, for example, be enabled for use with various wiredcommunication networks, e.g., such as telephone system, a local areanetwork, a wide area network, a personal area network, an intranet, theInternet, etc.

In accordance with certain example implementations communicationinterface 408 and/or 508 (see FIG. 5) may, for example, be enabled foruse with various wireless communication networks such as a wireless widearea network (WWAN), a wireless local area network (WLAN), a wirelesspersonal area network (WPAN), and so on. The term “network” and “system”may be used interchangeably herein. A WWAN may be a Code DivisionMultiple Access (CDMA) network, a Time Division Multiple Access (TDMA)network, a Frequency Division Multiple Access (FDMA) network, anOrthogonal Frequency Division Multiple Access (OFDMA) network, aSingle-Carrier Frequency Division Multiple Access (SC-FDMA) network, andso on. A CDMA network may implement one or more radio accesstechnologies (RATs) such as cdma2000, Wideband-CDMA (W-CDMA), TimeDivision Synchronous Code Division Multiple Access (TD-SCDMA), to namejust a few radio technologies. Here, cdma2000 may include technologiesimplemented according to IS-95, IS-2000, and IS-856 standards. A TDMAnetwork may implement Global System for Mobile Communications (GSM),Digital Advanced Mobile Phone System (D-AMBP capability), or some otherRAT. GSM and W-CDMA are described in documents from a consortium named“3rd Generation Partnership Project” (3GPP). Cdma2000 is described indocuments from a consortium named “3rd Generation Partnership Project 2”(3GPP2). 3GPP and 3GPP2 documents are publicly available. A WLAN mayinclude an IEEE 802.11x network, and a WPAN may include a Bluetoothnetwork, an IEEE 802.15x, for example. Wireless communication networksmay include so-called next generation technologies (e.g., “4G”), suchas, for example, Long Term Evolution (LTE), Advanced LTE, WiMAX, UltraMobile Broadband (UMB), and/or the like. Additionally, communicationinterface(s) 408 may further provide for infrared-based communicationswith one or more other devices. A WLAN may, for example, comprise anIEEE 802.11x network, and a WPAN may comprise a Bluetooth network, anIEEE 802.15x, for example. Wireless communication implementationsdescribed herein may also be used in connection with any combination ofWWAN, WLAN or WPAN.

Computing device 112 may, for example, further comprise one or moreinput and/or output units 414. Input and/or output units 414 mayrepresent one or more devices or other like mechanisms that may be usedto obtain inputs from and/or provide outputs to one or more otherdevices and/or a user. Thus, for example, input and/or output units 414may comprise various buttons, switches, a touch pad, a trackball, ajoystick, a touch screen, a keyboard, and/or the like, which may be usedto receive one or more user inputs. In certain instances, input and/oroutput units 414 may comprise various devices that may be used inproducing a visual output, an audible output, and/or a tactile outputfor a user. For example, input and/or output units 414 may be used topresent a video display, graphical user interface, etc., on a displaymechanism.

FIG. 5 is a schematic diagram illustrating certain features of anexample special purpose computing platform 500 that may be providedwithin mobile device 102 or apparatus 104 (FIG. 1) according to thevarious techniques provided herein.

As illustrated computing platform 500 may comprise one or moreprocessing units 502 (e.g., to perform data processing in accordancewith certain techniques provided herein) coupled to memory 504 via oneor more connections 506 (e.g., one or more electrical conductors, one ormore electrically conductive paths, one or more buses, one or morefiber-optic paths, one or more circuits, one or more buffers, one ormore transmitters, one or more receivers, etc.). Processing unit(s) 502may, for example, be implemented in hardware or a combination ofhardware and software. Processing unit(s) 502 may be representative ofone or more circuits configurable to perform at least a portion of adata computing procedure or process. By way of example but notlimitation, a processing unit may include one or more processors,controllers, microprocessors, microcontrollers, application specificintegrated circuits, digital signal processors, programmable logicdevices, field programmable gate arrays, or the like, or any combinationthereof.

Memory 504 may be representative of any data storage mechanism. Memory504 may include, for example, a primary memory 504-1 and/or a secondarymemory 504-2. Primary memory 504-1 may comprise, for example, a randomaccess memory, read only memory, etc. While illustrated in this exampleas being separate from the processing units, it should be understoodthat all or part of a primary memory may be provided within or otherwiseco-located and coupled with processing unit 502 or other like circuitrywithin mobile device 102. Secondary memory 504-2 may comprise, forexample, the same or similar type of memory as primary memory and/or oneor more data storage devices or systems, such as, for example, a diskdrive, an optical disc drive, a tape drive, a solid motion state memorydrive, etc.

In certain implementations, secondary memory may be operativelyreceptive of, or otherwise configurable to couple to, a non-transitorycomputer readable medium 520. Memory 504 and/or non-transitory computerreadable medium 520 may comprise instructions 522 for use in performingdata processing, e.g., in accordance with the applicable techniques asprovided herein.

Special purpose computing platform 500 may, for example, furthercomprise one or more communication interface 508. Communicationinterface 508 may, for example, comprise one or more wired and/orwireless network interface units, radios, modems, etc., represented hereby one or more receivers 510 and one or more transmitters 512. It shouldbe understood that in certain implementations, communication interface508 may comprise one or more transceivers, and/or the like. Further, itshould be understood that although not shown, communication interface508 may comprise one or more antennas and/or other circuitry as may beapplicable given the communication interface capability.

In accordance with certain example implementations, communicationinterface 508 may, for example, be enabled for use with various wiredcommunication networks, e.g., such as telephone system, a local areanetwork, a wide area network, a personal area network, an intranet, theInternet, etc.

Mobile device 102 may, for example, further comprise one or more inputand/or output units 514. Input and/or output units 514 may represent oneor more devices or other like mechanisms that may be used to obtaininputs from and/or provide outputs to one or more other devices and/or auser. Thus, for example, input and/or output units 514 may comprisevarious buttons, switches, a touch pad, a trackball, a joystick, a touchscreen, a keyboard, a microphone, a camera, and/or the like, which maybe used to receive one or more user inputs. In certain instances, inputand/or output units 514 may comprise various devices that may be used inproducing a visual output, an audible output, and/or a tactile outputfor a user. For example, input and/or output units 514 may be used topresent a video display, graphical user interface, positioning and/ornavigation related information, visual representations of electronicmap, routing directions, etc., via a display mechanism and/or audiomechanism.

Mobile device 102 may, for example, comprise one or more sensors 516.For example, sensor(s) 516 may represent one or more environmentalsensors, such as, e.g., a magnetometer or compass, a barometer oraltimeter, etc., and which may be useful for positioning and/ordetermining a motion state. For example, sensor(s) 516 may represent oneor more inertial sensors, which may be useful in detecting certainmovements of mobile device 102. Thus for example, sensor(s) 516 maycomprise one or more accelerometers, one or one or more gyroscopes.Further, in certain instances sensor(s) 516 may comprise and/or take theform of one or more input devices such as a sound transducer, amicrophone, a camera, a light sensor, etc.

SPS receiver 518 may be capable of acquiring and acquiring SPS signals134 via one or more antennas (not shown). SPS receiver 518 may alsoprocess, in whole or in part, acquired SPS signals 134 for estimating aposition and/or a motion of mobile device 102. In certain instances, SPSreceiver 518 may comprise one or more processing unit(s) (not shown),e.g., one or more general purpose processors, one or more digital signalprocessors DSP(s), one or more specialized processors that may also beutilized to process acquired SPS signals, in whole or in part, and/orcalculate an estimated location of mobile device 102. In certainimplementations, all or part of such processing of acquired SPS signalsmay be performed by other processing capabilities in mobile device 102,e.g., processing unit(s) 502, memory 504, etc., in conjunction with SPSreceiver 518. Storage of SPS or other signals for use in performingpositioning operations may be performed in memory 504 or registers (notshown).

In certain instances, sensor(s) 516 may generate analog or digitalsignals that may be stored in memory 504 and processed by DPS(s) (notshown) or processing unit(s) 502 in support of one or more applicationssuch as, for example, applications directed to positioning or navigationoperations based, at least in part, on one or more positioningfunctions.

Processing unit(s) 502 may comprise a dedicated modem processor or thelike that may be capable of performing baseband processing of signalsacquired and down converted at receiver(s) 510 of communicationinterface 508 or SPS receiver 518. Similarly, a modem processor or thelike may perform baseband processing of signals to be up converted fortransmission by (wireless) transmitter(s) 512. In alternativeimplementations, instead of having a dedicated modem processor, basebandprocessing may be performed by a general purpose processor or DSP (e.g.,general purpose and/or application processor). It should be understood,however, that these are merely examples of structures that may performbaseband processing, and that claimed subject matter is not limited inthis respect. Moreover, it should be understood that the exampletechniques provided herein may be adapted for a variety of differentelectronic devices, mobile devices, transmitting devices, environments,position fix modes, etc.

In accordance with certain aspects, an apparatus may be provided for usein a mobile device, which comprises: means for identifying two or moresubsets of grid points corresponding to an electronic map representing aparticular environment; means for selecting one of the two or moresubsets of grid points for use in position estimation based, at least inpart, on a motion state of the mobile device; and means for determiningan estimated position of the mobile device within the particularenvironment based, at least in part, on the selected subset of gridpoints. In certain instances, such an apparatus may further comprise oneor more of: means for receiving at least one previously identifiedsubset of grid points from at least one other device; means forreceiving a set of grid points corresponding to the electronic map fromat least one other device and means for subdividing the set of gridpoints to identify at least one of the two or more subsets of gridpoints; means for subdividing the set of grid points to identify atleast one of the two or more subsets of grid points based, at least inpart, on at least one motion state weighting parameter for at least onegrid point within the set of grid points; means for affecting at leastone motion state weighting parameter for at least one grid point withinat least one of the two or more subsets of grid points based, at leastin part, on the motion state of the mobile device; means fortransmitting at least one message to at least one other device, themessage being indicative of at least one of: the motion state of themobile device; a motion state weighting parameter for at least one gridpoint within at least one of the two or more subsets of grid points;and/or at least one motion state threshold value; and/or means fordetermining the motion state based, at least in part, on a signalobtained from a sensor of the mobile device.

In accordance with certain aspects, an article of manufacture may beprovided which comprises, a non-transitory computer-readable mediumhaving stored therein computer implementable instructions executable bya processing unit of a mobile device to: identify two or more subsets ofgrid points corresponding to an electronic map representing a particularenvironment; select one of the two or more subsets of grid points foruse in position estimation based, at least in part, on a motion state ofthe mobile device; and determine an estimated position of the mobiledevice within the particular environment based, at least in part, on theselected subset of grid points.

In accordance with certain aspects, an apparatus may be provided for usein a computing device, which comprises: means for obtaining a set ofgrid points corresponding to an electronic map representing a particularenvironment; means for subdividing the set of grid points to identifytwo or more subsets of grid points for use in position estimation by amobile device based, at least in part, on two or more motion statescorresponding to the mobile device; and means for transmitting at leastone of the two or more subsets of grid points to the mobile device. Incertain instances, such an apparatus may further comprise one of moreof: means for subdividing the set of grid points to identify at leastone of the two or more subsets of grid points based, at least in part,on at least one motion state weighting parameter for at least one gridpoint within the set of grid points; means for comparing at least onemotion state weighting parameter with at least one motion statethreshold value; means for identifying a first subset of grid points ofthe two or more subsets of grid points for use in position estimation bythe mobile device while in a transitional state and means foridentifying a second subset of grid points of the two or more subsets ofgrid points for use in position estimation by the mobile device while ina non-transitional state; means for identifying a first subset of gridpoints of the two or more subsets of grid points for use in positionestimation by the mobile device while in a first transitional state andmeans for identifying a second subset of grid points of the two or moresubsets of grid points for use in position estimation by the mobiledevice while in a second transitional state; and/or means for receivingat least one message from at least one mobile device, the message beingindicative of at least one of a motion state of the mobile device, amotion state weighting parameter for at least one grid point within atleast one of the two or more subsets of grid points, and/or at least onemotion state threshold value.

In accordance with certain aspects, an article of manufacture may beprovided which comprises a non-transitory computer-readable mediumhaving stored therein computer implementable instructions executable bya processing unit of a computing device to: obtain a set of grid pointscorresponding to an electronic map representing a particularenvironment; subdivide the set of grid points to identify two or moresubsets of grid points for use in position estimation by a mobile devicebased, at least in part, on two or more motion states corresponding tothe mobile device; and initiate transmission of at least one of the twoor more subsets of grid points to the mobile device.

The techniques described herein may be implemented by various meansdepending upon applications according to particular features and/orexamples. For example, such methodologies may be implemented inhardware, firmware, and/or combinations thereof, along with software. Ina hardware implementation, for example, a processing unit may beimplemented within one or more application specific integrated circuits(ASICs), digital signal processors (DSPs), digital signal processingdevices (DSPDs), programmable logic devices (PLDs), field programmablegate arrays (FPGAs), processors, controllers, micro-controllers,microprocessors, electronic devices, other devices units designed toperform the functions described herein, and/or combinations thereof.

In the preceding detailed description, numerous specific details havebeen set forth to provide a thorough understanding of claimed subjectmatter. However, it will be understood by those skilled in the art thatclaimed subject matter may be practiced without these specific details.In other instances, methods and apparatuses that would be known by oneof ordinary skill have not been described in detail so as not to obscureclaimed subject matter.

Some portions of the preceding detailed description have been presentedin terms of algorithms or symbolic representations of operations onbinary digital electronic signals stored within a memory of a specificapparatus or special purpose computing device or platform. In thecontext of this particular specification, the term specific apparatus orthe like includes a general purpose computer once it is programmed toperform particular functions pursuant to instructions from programsoftware. Algorithmic descriptions or symbolic representations areexamples of techniques used by those of ordinary skill in the signalprocessing or related arts to convey the substance of their work toothers skilled in the art. An algorithm is here, and generally, isconsidered to be a self-consistent sequence of operations or similarsignal processing leading to a desired result. In this context,operations or processing involve physical manipulation of physicalquantities. Typically, although not necessarily, such quantities maytake the form of electrical or magnetic signals capable of being stored,transferred, combined, compared or otherwise manipulated as electronicsignals representing information. It has proven convenient at times,principally for reasons of common usage, to refer to such signals asbits, data, values, elements, symbols, characters, terms, numbers,numerals, information, or the like. It should be understood, however,that all of these or similar terms are to be associated with appropriatephysical quantities and are merely convenient labels. Unlessspecifically motion stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “generating”, “obtaining”, “modifying”,“selecting”, “identifying”, and/or the like refer to actions orprocesses of a specific apparatus, such as a special purpose computer ora similar special purpose electronic computing device. In the context ofthis specification, therefore, a special purpose computer or a similarspecial purpose electronic computing device is capable of manipulatingor transforming signals, typically represented as physical electronic ormagnetic quantities within memories, registers, or other informationstorage devices, transmission devices, or display devices of the specialpurpose computer or similar special purpose electronic computing device.In the context of this particular patent application, the term “specificapparatus” may include a general purpose computer once it is programmedto perform particular functions pursuant to instructions from programsoftware.

The terms, “and”, “or”, and “and/or” as used herein may include avariety of meanings that also are expected to depend at least in partupon the context in which such terms are used. Typically, “or” if usedto associate a list, such as A, B or C, is intended to mean A, B, and C,here used in the inclusive sense, as well as A, B or C, here used in theexclusive sense. In addition, the term “one or more” as used herein maybe used to describe any feature, structure, or characteristic in thesingular or may be used to describe a plurality or some othercombination of features, structures or characteristics. Though, itshould be noted that this is merely an illustrative example and claimedsubject matter is not limited to this example.

While there has been illustrated and described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the art that various other modifications may be made, andequivalents may be substituted, without departing from claimed subjectmatter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from the central concept described herein.

Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within the scope of appendedclaims, and equivalents thereof.

What is claimed is:
 1. A method comprising, at a mobile device:identifying two or more subsets of grid points corresponding to anelectronic map representing a particular environment; selecting one ofsaid two or more subsets of grid points for use in position estimationbased, at least in part, on a motion state of said mobile device; anddetermining an estimated position of said mobile device within saidparticular environment based, at least in part, on said selected subsetof grid points.
 2. The method as recited in claim 1, wherein identifyingsaid two or more subsets of grid points comprises: receiving at leastone previously identified subset of grid points from at least one otherdevice.
 3. The method as recited in claim 1, wherein identifying saidtwo or more subsets of grid points comprises: receiving a set of gridpoints corresponding to said electronic map from at least one otherdevice; and subdividing said set of grid points to identify at least oneof said two or more subsets of grid points.
 4. The method as recited inclaim 3, and further comprising, at said mobile device: subdividing saidset of grid points to identify at least one of said two or more subsetsof grid points based, at least in part, on at least one motion stateweighting parameter for at least one grid point within said set of gridpoints.
 5. The method as recited in claim 1, wherein said two or moresubsets of grid points comprise: a first subset of grid pointsindicative of at least one grid point determined to represent a likelytransitional region within said particular environment; and a secondsubset of grid points indicative of at least one grid point determinedto represent a likely non-transitional region within said particularenvironment.
 6. The method as recited in claim 1, and furthercomprising, at said mobile device: affecting at least one motion stateweighting parameter for at least one grid point within at least one ofsaid two or more subsets of grid points based, at least in part, on saidmotion state of said mobile device.
 7. The method as recited in claim 1,and further comprising, at said mobile device: transmitting at least onemessage to at least one other device, said at least one message beingindicative of at least one of: said motion state of said mobile device;a motion state weighting parameter for at least one grid point within atleast one of said two or more subsets of grid points; and/or at leastone motion state threshold value.
 8. A mobile device comprising: memory;and a processing unit to: identify two or more subsets of grid pointsstored in said memory as corresponding to an electronic map representinga particular environment; select one of said two or more subsets of gridpoints for use in position estimation based, at least in part, on amotion state of said mobile device; and determine an estimated positionof said mobile device within said particular environment based, at leastin part, on said selected subset of grid points.
 9. The mobile device asrecited in claim 8, and further comprising: a communication interface;and said processing unit to further receive at least one previouslyidentified subset of grid points from at least one other device via saidcommunication interface.
 10. The mobile device as recited in claim 8,and further comprising: a communication interface; and said processingunit to further: receive a set of grid points corresponding to saidelectronic map from at least one other device via said communicationinterface; and subdivide said set of grid points to identify at leastone of said two or more subsets of grid points.
 11. The mobile device asrecited in claim 8, wherein said two or more subsets of grid pointscomprise: a first subset of grid points indicative of at least one gridpoint determined to represent a likely transitional region within saidparticular environment; and a second subset of grid points indicative ofat least one grid point determined to represent a likelynon-transitional region within said particular environment.
 12. Themobile device as recited in claim 8, said processing unit to affect atleast one motion state weighting parameter for at least one grid pointwithin at least one of said two or more subsets of grid points based, atleast in part, on said motion state of said mobile device.
 13. Themobile device as recited in claim 8, and further comprising: acommunication interface; and said processing unit to further initiatetransmission of at least one message to at least one other device viasaid communication interface, said at least one message being indicativeof at least one of: said motion state of said mobile device; a motionstate weighting parameter for at least one grid point within at leastone of said two or more subsets of grid points; and/or at least onemotion state threshold value.
 14. The mobile device as recited in claim8, and further comprising: a sensor; and said processing unit to furtherdetermine said motion state based, at least in part, on a signalobtained from said sensor.
 15. The mobile device as recited in claim 8,wherein at least one of said two or more subsets of grid points areidentified in a routing graph corresponding to at least a portion ofsaid electronic map.
 16. A method comprising, at a computing device:obtaining a set of grid points corresponding to an electronic maprepresenting a particular environment; subdividing said set of gridpoints to identify two or more subsets of grid points for use inposition estimation by a mobile device based, at least in part, on twoor more motion states corresponding to said mobile device; andtransmitting at least one of said two or more subsets of grid points tosaid mobile device.
 17. The method as recited in claim 16, and furthercomprising, at said computing device subdividing said set of grid pointsto identify at least one of said two or more subsets of grid pointsbased, at least in part, on at least one motion state weightingparameter for at least one grid point within said set of grid points.18. The method as recited in claim 17, wherein said at least one motionstate weighting parameter is based, at least in part, on at least onemotion state historical record corresponding to one or more mobiledevices.
 19. The method as recited in claim 17, and further comprising,at said computing device comparing said at least one motion stateweighting parameter with at least one motion state threshold value. 20.The method as recited in claim 16, and further comprising, at saidcomputing device: identifying a first subset of grid points of said twoor more subsets of grid points for use in position estimation by saidmobile device while in a transitional state; and identifying a secondsubset of grid points of said two or more subsets of grid points for usein position estimation by said mobile device while in a non-transitionalstate.
 21. The method as recited in claim 16, and further comprising, atsaid computing device: identifying a first subset of grid points of saidtwo or more subsets of grid points for use in position estimation bysaid mobile device while in a first transitional state; and identifyinga second subset of grid points of said two or more subsets of gridpoints for use in position estimation by said mobile device while in asecond transitional state.
 22. The method as recited in claim 16, andfurther comprising, at said computing device: receiving at least onemessage from at least one mobile device, said at least one message beingindicative of at least one of: a motion state of said mobile device; amotion state weighting parameter for at least one grid point within atleast one of said two or more subsets of grid points; and/or at leastone motion state threshold value.
 23. A computing device comprising: acommunication interface; and a processing unit to: obtain a set of gridpoints corresponding to an electronic map representing a particularenvironment; subdivide said set of grid points to identify two or moresubsets of grid points for use in position estimation by a mobile devicebased, at least in part, on two or more motion states corresponding tosaid mobile device; and initiate transmission of at least one of saidtwo or more subsets of grid points to said mobile device via saidcommunication interface.
 24. The computing device as recited in claim23, and said processing unit to: subdivide said set of grid points toidentify at least one of said two or more subsets of grid points based,at least in part, on at least one motion state weighting parameter forat least one grid point within said set of grid points.
 25. Thecomputing device as recited in claim 24, wherein said at least onemotion state weighting parameter is based, at least in part, on at leastone motion state historical record corresponding to one or more mobiledevices.
 26. The computing device as recited in claim 24, and saidprocessing unit to: compare said at least one motion state weightingparameter with at least one motion state threshold value.
 27. Thecomputing device as recited in claim 23, wherein said two or moresubsets of grid points comprise at least one subset of grid pointsindicative of at least one grid point determined to represent a likelytransitional region within said particular environment.
 28. Thecomputing device as recited in claim 23, wherein said two or moresubsets of grid points comprise at least one subset of grid pointsindicative of at least one grid point determined to represent a likelynon-transitional region within said particular environment.
 29. Thecomputing device as recited in claim 23, and said processing unit to:receive at least one message from at least one mobile device via saidcommunication interface, said at least one message being indicative ofat least one of: a motion state of said mobile device; a motion stateweighting parameter for at least one grid point within at least one ofsaid two or more subsets of grid points; and/or at least one motionstate threshold value.
 30. The computing device as recited in claim 23,wherein at least one of said two or more subsets of grid points areidentified in a routing graph corresponding to at least a portion ofsaid electronic map, and said particular environment comprises an indoorenvironment.